Affinity-based proteomics offers opportunities for the discovery and validation of disease-associated proteins in human body fluids. This thesis describes the use of antibody-based immunoassays for multiplexed analysis of proteins in human plasma, serum and cerebrospinal fluid (CSF). This high-throughput method was applied with the objective to identify proteins associated to clinical variables. The main work in this thesis was conducted within the diseases of multiple sclerosis and malignant melanoma, as well as mammographic density, a risk factor for breast cancer. The suspension bead array (SBA) technology has been the main method for the work presented in this thesis (Paper I-IV). SBA assays and other affinity proteomic technologies were introduced for protein profiling of sample material obtained from clinical collaborators and biobanks. Perspectives on the validation of antibody selectivity by means of e.g. immuno-capture mass spectrometry are also provided. Paper I describes the development and application of a protocol for multiplexed pro- tein profiling of CSF. The analysis of 340 CSF samples from patients with multiple sclerosis and other neurological disease revealed proteins with potential association to disease progression (GAP43) and inflammation (SERPINA3). Paper II continued on this work with an extended investigation of more than 1,000 clinical samples and included both plasma and CSF collected from the same patients. Comparison of disease subtypes and controls revealed five plasma proteins of potential diagnostic relevance, such as IRF8 and GAP43. The previously reported associations for GAP43 and SERPINA3 in CSF was confirmed. Subsequent immunohistochemical analysis of post-mortem brain tissue revealed differential protein expression in disease affected areas. In Paper III, 150 serum samples from patients with cutaneous malignant melanoma were analyzed. Protein profiles from antibody bead arrays suggested three proteins (RGN, MTHFD1L, STX7) of differential abundance between patients with no disease recurrence and low tumor thickness (T-stage 1 and 2) compared to patients with high tumor thickness (T-stage 3 and 4) and disease recurrence. We observed MTHFD1L expression in tissue of a majority of patients, while expression of STX7 in melanoma tissue had been reported previously. Paper IV describes the analysis of protein in plasma in relation to mammographic breast density (MD), one of the strongest risk factors for the development of breast cancers. More than 1,300 women without prior history of breast cancer were screened. Linear associations to MD in two independent sample sets were found for 11 proteins, which are expressed in the breast and involved in tissue homeostasis, DNA repair, cancer development and/or progression in MD. In conclusion, this thesis describes the use of multiplexed antibody bead arrays for protein profiling of serum, plasma and CSF, and it shortlists disease associated proteins for further validation studies. / <p>QC 20170302</p>

Nous avons étudié l’utilisation de détecteurs spectrométriques, qui émergent dans le domaine de l’imagerie médicale, pour leur application à la mammographie. Ces détecteurs permettent de discriminer l’énergie des photons reçus, ce qui apporte une information supplémentaire à l’imagerie d’atténuation traditionnelle. Ainsi, il est possible d’utiliser des techniques de décomposition en base de deux matériaux, notamment pour déterminer la densité glandulaire dans le sein, qui correspond au pourcentage de tissus glandulaires, et qui est un facteur de risque pour le développement d’un cancer, à partir d’une seule irradiation. Jusqu’alors, il était possible d’utiliser cette méthode à partir de deux expositions à deux énergies distinctes. Dans certains cas, une nouvelle tendance consiste à pratiquer des mammographies avec injection d’un produit de constratse iodé, mais cela nécessite également au moins deux irradiations. Nous avons donc proposé d’estimer la densité du sein et la concentration d’iode simultanément, à partir d’une seule irradiation, à une dose 0,93 mGy, en appliquant des méthodes de décomposition en base de trois matériaux. Premièrement, des méthodes polynomiales ont été adaptées pour être comptibles avec l’information spectrale provenant de 3 canaux d’énergies. Ensuite, nous avons montré qu’une deuxième approche, capable de prendre en compte une information spectrale plus fine, basée sur la maximisation de la vraisemblance entre un spectre mesuré et des spectres de références, était capable d’atteindre de meilleurs résultats. Enfin, nous avons développé une méthode capable de prendre en compte la compression du sein en mammographie pour améliorer les résultats obtenus par la méthode de maximum de vraisemblance. / Energy sensitive X-ray detectors are emerging in the field of medical imaging. We have investigated the use of this new type of X-ray detectos for their application to mammography exams. These detectors are able to discriminate the energy of received photons, which provides additional information to a standard mammography image only composed of the total attenuation signal. Thus, these detectors allow the use of basis material decomposition techniques, from a single x-ray exposure, and permit to determine the breast density, which corresponds to the percentage of glandular tissues in the breast. Breast density is known for being a risk factor for the development of breast cancers. Without energy sensitive X-ray detectors, this method requires two X-ray exposures at different energies. Contrast enhanced mammography is also developing but it requires the use an iodinated contrast media and at least two irradiations. Hence, we proposed to take benefit of energy-sensitive detectors to simultaneously estimate the breast density and the iodine concentration, using a single X-ray exposure at a mean glandular dose of 0.93 mGy. This approach is based on three basis material decomposition methods. First, different polynomial methods have been adapted to comply with spectral information from 3 energy channels. Then, we showed that a second approach, based on the maximisation of the likelihood between a measured spectrum and reference spectra, was able take into consideration finer spectral information and achieved better results. Finally, we have developed a method that can take into consideration the thickness of the compressed breast during a mammography exam to improve the results obtained by the maximum likelihood method.

Made available in DSpace on 2016-08-19T18:15:54Z (GMT). No. of bitstreams: 1 Ana Maria Nogueira Silva.pdf: 441134 bytes, checksum: 7fdca83931c0bdbbdf8528fe5b39b167 (MD5) Previous issue date: 2007-12-22 / Objetives: To assess the effects between non-treatment (placebo group) and a low dosage estrogen-progestin regimen with norgestimate on changes in mammographic breast density (BD) in postmenopausal women after 12 months of hormone therapy. Methods: A prospective study was performed with 40 postmenopausal patients from Materno-Infantil University Hospital (São Luís, Maranhão), divided into two groups: treated ( n=20) using 1 mg of beta-estradiol (E2) and 1mg of E2 + 90mcg norgestimate (NMG); and control (placebo). One-hundred sixty mammograms were done before and after a 12-month period of hormone therapy and BD between the two exams in each group was compared. BD was measured by two qualitative methods (Wolfe and Breast Image Reporting and Data System BI-RADS classification) by two different observers. Data were analysed using Epi- Info program, with statistical significance of 5%. Interobserver variability from mammograms was considered low in both classifications, as well as there were a high percentage of agreement between the two methods. T-student test was used for means and Fisher test for binomial variables. Results: Both groups were considered homogeneous. Body mass index (BMI) did not change during the study period in both groups. Mammographic breast density s classification according to Wolfe was respectively in treated and placebo groups, N1=12, P1=5, P2=3, DY=0; and N1=11, P1=6, P2=3, DY=0, before and after low dose hormone therapy, with no significant differences. A similar pattern was observed at placebo group using Wolfe classification. There were no significant changes in BD according to BI-RADS category in both groups. Conclusion: Low dosage hormone therapy with norgestimate was not associated with increased BD after 12 months of treatment, supporting current literature. Further studies using devices with better technology in analyzing BD are needed to confirm a stability of breast epithelium with different types of low dosage hormone therapy. / Objetivos: Avaliar mudanças no padrão da densidade mamográfica (DM) com a utilização da terapia estro-progestativa de baixa dose com norgestimato entre mulheres na pós-menopausa durante um período de 1 ano. Metodologia: Realizado estudo prospectivo com 40 pacientes menopausadas do Hospital Universitário Materno-Infantil (São Luís, Maranhão), divididas em dois grupos: tratado (n=20) usando 1 mg de beta-estradiol (E2) e 1mg de E2 + 90mcg de norgestimato (NMG); e controle (placebo). Cento e sessenta mamografias foram realizadas antes e depois de 12 meses de acompanhamento. A DM foi aferida por dois métodos qualitativos (classificação de Wolfe e do Breast Image Reporting and Data System BI-RADS) por dois observadores. Os dados foram analisados e tabulados utilizando-se o programa Epi-Info (alfa=5%). A variabilidade interobservador foi considerada baixa nas duas classificações, assim como houve ótima concordância entre os dois métodos. Os testes t de Student e Fisher foram utilizados para, respectivamente, médias e variáveis binomais. Resultados: Ambos os grupos foram considerados homogêneos. O índice de massa corpórea (IMC) não se alterou durante o período do estudo tanto no grupo A como no B. A classificação de DM no grupo tratado, de acordo com Wolfe foi, respectivamente: N1=12, P1=5, P2=3, DY=0; e N1=11, P1=6, P2=3, DY=0, respectivamente antes e depois da terapia hormonal de baixa dose, sem diferenças estatísticas. Um padrão similar foi também observado no grupo controle. Não houveram mudanças significativas na densidade mamária de acordo com a classificação BI-RADS nos dois grupos. Conclusão: A terapia hormonal de baixa dose com norgestimato não foi associada com aumento de DM após 12 meses de tratamento, ratificando literatura corrente. Há necessidade de melhores tecnologias para avaliar a DM e confirmar a estabilidade do epitélio mamário com diferentes tipos de terapia hormonal de baixa dose.

Made available in DSpace on 2016-08-16T18:18:41Z (GMT). No. of bitstreams: 1 TESE Lucio Flavio de Albuquerque Campos.pdf: 2195360 bytes, checksum: 81d7dbefdd6a7602831593716a16b445 (MD5) Previous issue date: 2013-06-14 / Breast Cancer remains the type of cancer with the largest incidence and mortality in women. The best method of prevention is early diagnosis, which is carried out with mammography. However, a mammogram is not effective when the breast has a composition of greater than 50% fibroglandular tissue, or dense tissue. Studies show that high breast density is identified as a risk factor for developing the disease, and because of this new diagnostic technique for cancer in patients with dense breasts are being studied. This thesis proposes a method for early diagnosis of cancer in dense breasts, considered in the literature as hard scanning and detection. The methodology applied in this work used MIAS database for tests, equalization adaptive of histogram and contrast stretching techniques for segmentation step, and independent component analysis maxima-relevance-minimal-redundance and support vector machine for classification step. The tests were carried out with 76 breast mammograms whose dense parenchyma s make detection difficult. From the tests, we obtained accuracy of 97.36% in the segmentation stage. Already in the classification stage was an accuracy of 97.2% with a sensitivity of 81.88% and specificity of 100%. Based on the results, considering that the method was performed only on mammograms difficult to detect, it can be considered that the method achieved excellent performance, justifying the test in larger databases, and eventually enabling their use in hospitals and radiology clinics. / O câncer de mama continua sendo o tipo de câncer de maior incidência e mortalidade entre as mulheres. O melhor método de prevenção é o diagnóstico precoce, que é realizado com o auxilio da mamografia. Contudo, a mamografia não é eficaz quando a mama apresenta uma composição superior a 50 % de tecido fibroglandular, ou seja, de tecido denso. Estudos comprovam que a densidade mamária elevada é apontada como um fator de risco para o desenvolvimento da doença, e devido a isso novas técnicas de diagnóstico de câncer em pacientes com mamas densas estão sendo estudados. Esta tese propõe um método de diagnóstico precoce de câncer, em mamas densas, consideradas pela literatura de difícil rastreio e detecção, com o objetivo de aumentar as chances de cura da paciente, e diminuir os casos de mortalidade da doença. A metodologia empregada no trabalho utilizou a base de dados MIAS para teste, técnicas de equalização adaptativa e alargamento de contraste, na fase de segmentação, e análise de componentes independentes, máxima relevância - mínima redundância e máquinas de vetor de suporte, na etapa de classificação. Os testes foram realizados com 76 mamogramas de mamas em que o parênquima denso dificulta a detecção. A partir dos testes realizados, obteve-se média de acerto de 97.36 % na etapa de segmentação. Já na etapa de classificação foi encontrada uma média de acerto de 97,2% com sensibilidade de 81.88% e especificidade de 100%. Baseado nos resultados encontrados, considerando que o método foi realizado apenas em mamogramas de difícil detecção, pode-se considerar que o método obteve excelente desempenho, justificando o teste em bases de dados maiores, e futuramente viabilizando seu uso em hospitais e clinicas de radiologia.

Cette thèse avait deux buts principaux : a) l'implémentation et l'amélioration d'une méthode de calcul de densité volumique du sein (VBD), et b) la proposition d'une mesure d'irradiation utilisable pour l'évaluation du risque individuel en mammographie avec une méthode pour l'estimer. La densité du sein est connue comme indicateur de risque du cancer. Une méthode de quantification objective de la VBD a été développée, à partir d'approches existantes, et améliorée. La méthode a été implémentée pour deux systèmes de mammographie. Elle repose sur l'étalonnage du système de mammographie et la chaîne d'acquisition avec des fantômes équivalents aux tissus mammaires. Une carte de densité est calculée.La contribution majeure de la thèse consiste en une nouvelle méthode de validation, applicable à tout calcul de VBD d'image de mammographie. Elle consiste à comparer les résultats aux valeurs de densité obtenues par des scanners thoraciques pour la même patiente. Cette validation a été appliquée à notre méthode de calcul et nous avons trouvé 10% d'écart moyen entre les deux méthodes, ce qui est comparable aux résultats de l'état de l'art. Pour le risque d'irradiation individuel, nous proposons de remplacer la dose glandulaire moyenne par l'énergie déposée, qui dépend de la quantité et de la distribution du tissu glandulaire, qui est le tissu à risque. L'énergie volumique déposée est calculée par simulation de Monte Carlo. Le VBD, calculé pour l'image de projection à 0° en tomosynthèse, aide à localiser le tissu glandulaire et à attribuer l'énergie déposée dans les tissus différents. Une proposition a été faite pour des fantômes géométriques, un fantôme texturé et un cas de patiente / In this research project the main goals were a) to implement a method for the computation of the volumetric breast density (VBD), and b) to propose an improved quantity for the assessment of individual radiation-induced risk, in particular during mammography, together with a method to quantify it. The breast density is known as a breast cancer risk factor. The objective quantification of the volumetric breast density was developed, based on already published methods, and improved. The method was implemented for two mammography systems. It is based on the calibration of the mammography system acquisition chain with breast equivalent phantoms and computes a breast density map. Our most important contribution resides in a new validation method applicable to any VBD computation, consisting in comparing its results with the VBD obtained from a thorax CT examination for the same patient. This validation method was applied to our VBD computation. We found an average deviation between mammography and CT of less than 10%. Our results are comparable to the state-of-the-art results for other validation methods. For the individual radiation risk, we proposed to replace the average glandular dose by the imparted energy, which depends on the quantity and distribution of the glandular tissue, which is the tissue at risk. The volumetric imparted energy is computed from Monte Carlo simulations. The VBD, computed for the 0° projection of tomosynthesis exams, helps us to localize the glandular tissue and to attribute the imparted energy to the different tissues. A proposition was implemented for geometric phantoms, a textured phantom and a patient case.

Αντικείμενο της παρούσας εργασία είναι ο υπολογισμός και η ταξινόμηση, με βάση το σύστημα, BIRADS της πυκνότητας του μαστού από εικόνες μαστογραφίας. Στα πλαίσια της προσπάθειας αυτής αναπτύχθηκε ολοκληρωμένο υπολογιστικό σύστημα σε γραφικό περιβάλλον ως λογισμικό πακέτο, σε γλώσσα Visual C++ .NET . Το υπολογιστικό αυτό σύστημα δέχεται σαν είσοδο εικόνες μαστογραφίας σε οποιοδήποτε από τα δημοφιλή bitmap format εικόνων όπως jpeg και tiff καθώς και DICOM αρχεία. Η λειτουργία του μπορεί να χωριστεί σε τρία στάδια: το στάδιο της προεπεξεργασίας, το στάδιο απομόνωσης της περιοχής του μαστού και το στάδιο καθορισμού της πυκνότητας του μαστού. Στο πρώτο στάδιο παρέχονται μια σειρά από στοιχειώδη εργαλεία επεξεργασίας εικόνας όπως εργαλεία περιστροφής, αποκοπής και αλλαγής αντίθεσης . Επιπρόσθετα παρέχεται η δυνατότητα Ανισοτροπικού Φιλτραρίσματος της εικόνας. Στο δεύτερο στάδιο γίνεται η απομόνωση της περιοχής του μαστού είτε απευθείας από τον χρήστη είτε αυτόματα με χρήση των ιδιοτήτων του μονογονικού (monogenic) σήματος για την αφαίρεση του παρασκηνίου (background) καθώς και κυματιδίων Gabor για τον διαχωρισμού του θωρακικού μυός. Στο τρίτο στάδιο παρέχεται η δυνατότητα ταξινόμησης της πυκνότητας του μαστού από τον χρήστη με τον καθορισμό κατάλληλου κατωφλίου των επιπέδων γκρίζου της εικόνας αλλά και η δυνατότητα αυτόματης ταξινόμησης της πυκνότητας του μαστού κατά BIRADS με χρήση Δομικών Στοιχείων Υφής (textons) και της τεχνικής pLSA. Όλες οι παραπάνω λειτουργίες παρέχονται μέσω μίας κατά το δυνατόν φιλικότερης προς τον χρήστη διεπαφής. / The present thesis aims at the classification of breast tissue according to BIRADS system based on texture features. To this end an integrated software system was developed in visual C ++. The system takes as inputs pictures in most of the popular bitmap formats like .jpeg and .till as well as DICOM. The functionality of the system is provided by three modules: (a) pre-processing module, (b) breast segmentation module and (c) the breast tissue density classification module. In the pre-processing module a set tools for image manipulation (rotation, crop, gray level adjustment) are available which are accompanied by the ability to perform anisotropic filtering to the input image. In the second module, the user has the ability to interactively define the actual borders of the breast or ask the system to perform it automatically. Automatic segmentation is a two step procedure; in the first step breast tissue is separated from its background by using the characteristics of monogenic signals, while in the second step the pectoral muscle region is subtracted using Gabor wavelets. In the density classification module the user can either ask for a calculation of breast density based on user-defined grey level threshold or perform an automatic BIRADS-based classification using texture characteristics in conjunction with Probabilistic Latent Semantic Analysis (pLSA) algorithm. Special emphasis was given to the development of a functional and user-friendly interface.